code: def image_preprocess(self, image):
      with tf.name_scope("image_preprocess"):
          if image.dtype.base_dtype != tf.float32:
              image = tf.cast(image, tf.float32)
          mean = [0.485, 0.456, 0.406]  # rgb
          std = [0.229, 0.224, 0.225]
          if self.image_bgr:
              mean = mean[::-1]
              std = std[::-1]
          image_mean = tf.constant(mean, dtype=tf.float32) * 255.0
          image_std = tf.constant(std, dtype=tf.float32) * 255.0
          image = (image - image_mean) / image_std
          return image
  
imports: import multiprocessing
  import numpy
  import os
  from abc import abstractmethod
  import cv2
  import tqdm
  from tensorpack import tfv1
  from tensorpack import ModelDesc
  from tensorpack.dataflow import AugmentImageComponent, BatchData, MultiThreadMapData, MultiProcessRunnerZMQ, dataset, imgaug
  from tensorpack.input_source import QueueInput, StagingInput
  from tensorpack.models import regularize_cost, l2_regularizer
  from tensorpack.predict import FeedfreePredictor, PredictConfig
  from tensorpack.tfutils.summary import add_moving_summary
  from tensorpack.tfutils.optimizer import AccumGradOptimizer
  from tensorpack.utils import logger
  from tensorpack.utils.stats import RatioCounter
label_desc: Use image node_id 5 for calling the ImageNetModel obj's underlying member method code with example usage: obj.image_preprocess(image) and returns: image